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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,196 Documents
 114   115   116   117   118 
Digital Transformation of Electricity Bill Collection: Predicting Delays Using Machine Learning Utami, Dyah Puspita Sari Nilam; Arifyanto, Mochamad Ikbal
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14340

Abstract

Delays in electricity bill payments pose a significant challenge for PLN in maintaining financial stability and delivering equitable service quality to the public. This study aims to develop a payment delay prediction system to assist PLN UP3 Makassar Utara in prioritizing invoice distribution to customers with a high likelihood of late payments. The Random Forest algorithm was chosen for its ability to handle complex data and produce reliable predictions. This research analyses historical electricity customer data from 2018 to 2023, encompassing 227,163 entries. The data is processed using validation techniques such as K-Fold Validation and Rolling Window Validation to ensure the accuracy and generalizability of the model. The study's findings demonstrate that an accurate payment delay prediction model can be developed using the Random Forest method, incorporating historical features such as lag features, moving averages, and seasonal variables. Additionally, the system prioritizes invoice delivery to high-risk customers based on risk scores derived from historical delay patterns. This system reduces payment arrears at PLN UP3 Makassar Utara through proactive measures such as early notifications, personalized reminders, or payment incentives to encourage timely payments. As a result, the study indicates that the system effectively enhances the efficiency of payment management and supports the company's financial stability. However, the research is limited by the use of data from a single region, the absence of external factors in the model, and the high computational requirements. For broader implementation, further research should include data from other regions, consider external factors, and optimize computational resource usage.
A Comparative Analysis of Clustering Algorithms for Expedia’s Travel Dataset Airlangga, Gregorius
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14343

Abstract

The effective segmentation of travel data is crucial for deriving actionable insights in the tourism and hospitality sectors. This study conducts a comprehensive evaluation of four clustering algorithms Agglomerative Clustering, DBSCAN, Gaussian Mixture Models (GMM), and KMeans on a travel dataset, using three widely recognized metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Score. The dataset was preprocessed through standardization and dimensionality reduction via Principal Component Analysis (PCA) to facilitate visualization and ensure computational efficiency. The results highlight significant differences in the performance of these algorithms. Agglomerative Clustering achieved the highest Silhouette Score, indicating superior cluster cohesion and separation, while KMeans recorded the highest Calinski-Harabasz Score, demonstrating strong inter-cluster variance. In contrast, DBSCAN performed poorly, producing low scores across all metrics, attributed to sensitivity to parameter selection and density irregularities in the dataset. Gaussian Mixture Models exhibited moderate performance but struggled with overlapping clusters due to limitations in modeling non-Gaussian data distributions. Visualization of clustering results confirmed these findings, revealing compact clusters for Agglomerative and KMeans, while DBSCAN and GMM showed less defined structures. This study underscores the importance of selecting clustering algorithms based on dataset characteristics and analysis objectives
Comparative Analysis of Random Forest and SVM Performance in Asthma Prediction Zuhria, Lailatuz; Azwar Riza Habibi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14346

Abstract

This study evaluates the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in predicting asthma risk to identify the most suitable method for medical datasets. Key metrics include training time, testing time, forecasting time, error rate, and accuracy. The datasets involve attributes such as age and clinical factors, analyzed in three stages: training, testing, and forecasting. During training, SVM demonstrated faster processing, requiring a maximum of 0.5323 seconds compared to RF's 3.7736 seconds on 90% training data. In testing, RF excelled in speed, achieving a minimum testing time of 0.0215 seconds, while SVM required 0.0984 seconds on 10% test data. However, SVM consistently outperformed RF in error rate, with a minimum error rate of 19.17%, compared to RF's 25.10%. During training, SVM demonstrated faster processing, requiring a maximum of 0.5323 seconds compared to RF's 3.7736 seconds on 90% training data. In testing, RF excelled in speed, achieving a minimum testing time of 0.0215 seconds, while SVM required 0.0984 seconds on 10% test data. However, SVM consistently outperformed RF in error rate, with a minimum error rate of 19.17%, compared to RF's 25.10%.
A Comparative Study of Ensemble Learning and Neural Networks for the Heart Disease Prediction Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Lim, Bobi Hartanto Pramudita
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14347

Abstract

Heart disease continues to be a leading global cause of death, making the development of predictive models for early diagnosis a critical task. This study investigates the performance of various machine learning and deep learning models for heart disease prediction using a structured dataset of 918 observations and 11 features. The analysis includes ensemble methods like Random Forest, Gradient Boosting, and XGBoost, as well as neural networks such as Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). Traditional classifiers, including Support Vector Machines (SVM) and Logistic Regression, are also considered for benchmarking. The dataset was preprocessed using label encoding, standardization, and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and ensure data consistency. Model evaluation was conducted using key metrics such as precision, recall, F1-score, and ROC-AUC. The results demonstrated that ensemble methods, particularly Random Forest (ROC-AUC: 0.9313) and Gradient Boosting (ROC-AUC: 0.9279), consistently delivered superior performance. Among neural networks, MLPs showed promising results (ROC-AUC: 0.9232), outperforming CNNs, which were less effective in handling tabular data. Meanwhile, TabNet was found to be unsuitable for this dataset, as it significantly underperformed across all metrics. This research highlights the effectiveness of ensemble methods and MLPs in heart disease prediction and the importance of proper preprocessing techniques. Future work could focus on integrating hybrid models or advanced optimization techniques to further enhance predictive accuracy in clinical settings.
Graph Regularized Probabilistic Latent Semantic Analysis for Topic Analysis Using Social Media Data Muslim, Muhammad Panji; Hadi, Novi Trisman; Adrezo, Muhammad
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14348

Abstract

In today's digital era, social media data provides valuable insights into public opinion. This study implements the Graph Regularized Probabilistic Latent Semantic Analysis (GPLSA) method to analyze topics from social media data surrounding the 2024 Indonesian Presidential Election (Pemilu), as well as to evaluate the efficiency of the Probabilistic Latent Semantic Analysis (PLSA) algorithm. The research stages include collecting social media data on presidential debates and elections, text pre-processing, and applying the GPLSA method to identify main topics. The analysis results show that PLSA without graph achieved a topic coherence score of 0.653, indicating good consistency, while GPLSA decreased to 0.5, suggesting that the addition of graph regularization did not significantly enhance coherence. Additionally, PLSA without graph achieved a perplexity score of 12.138, indicating good predictive capability, while GPLSA increased to 12.511, showing that graph regularization did not improve the prediction of new words. PLSA without graph also produced topics relevant to election issues, while GPLSA generated topics influenced by graph regularization, though without significant improvement in topic quality. Sentiment analysis of social media posts provides insights into public responses to debates and election issues. Validation of the GPLSA model ensures relevant topic representation. This research contributes to the development of text analysis methods and offers valuable information for elections and democratic participation. These results can be utilized by stakeholders to make more strategic and informed decisions.
Optimization of Player Experience and Enemy AI using A* Algorithm in Game Arifudin, Dani; Fransjaya, Michael; El Fakhry , Yusif; Syahrizaldy, Hikmalul A’la
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14349

Abstract

The gaming industry is rapidly evolving, where engaging and challenging gameplay has become a key factor in a game's success. Effective enemy intelligence can enhance challenges and enrich the player experience. This study aims to improve the player experience and enemy intelligence in the game Galang the EcoRescue through the implementation of the A-star (A*) algorithm. A* is a pathfinding algorithm that uses distance estimation to find the shortest path to a target by utilizing a heuristic function. This game was developed using the Unity Engine, with the implementation of the A* algorithm to determine enemy movements and adapt their behavior according to the game’s situation. Testing was conducted to ensure improvements in both the player experience and enemy intelligence. The results of the study show that the A* algorithm successfully enhanced enemy intelligence by creating more realistic and adaptive movements in response to the player, ultimately providing more dynamic challenges and improving overall gameplay quality. This study utilized the Game Development Life Cycle (GDLC) method, covering the stages of initiation, pre-production, production, testing, beta, and release. The A* algorithm has proven to optimize the player experience in Galang the EcoRescue.
Decision-Making Framework Using MARCOS for Evaluating Sealing Machines in Small and Medium Enterprises Dewi, Marysca Shintya; Marlinda, Linda; Komarudin, Komarudin
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14367

Abstract

In the era of globalization, Micro, Small, and Medium Enterprises (MSMEs) hold a vital position in Indonesia's economy, contributing significantly to GDP and employment. Despite their importance, MSMEs need help in selecting appropriate sealer machines, which affects production efficiency and product quality. There are six different kinds of sealer machines that are looked at in this study. They are manual, vertical, continuous, horizontal semi-automatic, impulse, and vacuum. The MARCOS method is used to find the best option. Results indicate that the Impulse Sealer Machine (A5) is the most suitable, with a Ki value of 1.7, followed by the Continuous Sealer Machine (A3), with a Ki of 1.63. Machines such as Manual (A1), Vertical (A2), and Vacuum (A6) scored 1.6, while the Horizontal Semi-Automatic Sealer Machine (A4) ranked lowest at 1.36. These findings provide MSMEs with practical guidance for selecting sealer machines that enhance production efficiency and competitiveness in the global market while also contributing to the development of packaging technology research.
Improving Tesseract OCR Accuracy Using SymSpell Algorithm on Passport Data Had, Iqbaluddin Syam; Maulana Baihaqi, Wiga; Putriana Nuramanah Kinding, Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14395

Abstract

Optical Character Recognition (OCR) is a technology used to recognize text from images or digital documents, such as passports. One popular OCR tool is Tesseract as it offers high accuracy. However, OCR accuracy is often affected by various factors, including image noise and/or non-text elements. This article discusses the application of the SymSpell algorithm for post processing to improve OCR accuracy on standard Indonesian passports. OCR will be focused on the Visual Inspection Zone, specifically the Place of Birth and Issuing Office values. Unlike the Machine Readable Zone which is composed of individual codes and a clear background, the Visual Inspection Zone often experiences OCR errors due to holograms blocking the text and spaced layouts. SymSpell is an edit distance based spelling correction algorithm designed to process data quickly and efficiently, even on very huge datasets. In this study, SymSpell is used to detect and correct errors in OCR results that are compared to a corpus word list. Experimental results with 10 tested scans and passport photos showed that the integration of SymSpell with the Research and Development methodology was able to improve the OCR accuracy rate by 21,43% for certain Place of Birth and Issuing Office data from the Visual Inspection Zone. With this approach, OCR systems can provide more reliable results for practical applications.
Comparative Analysis of Homogeneous and Heterogeneous Ensembles for Diabetes Classification Optimization Maulana, Muhammad Naufal; Muljono, Muljono; Meindiawan, Eka Putra Agus
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14439

Abstract

Diabetes mellitus is a chronic disease with an increasing prevalence worldwide, including in Indonesia, reaching 11.7% by 2023. Early prediction of this disease is essential for more effective management. This study aims to develop a diabetes mellitus prediction model using an ensemble learning approach, including homogeneous (boosting and bagging) and heterogeneous (stacking and blending) techniques. In this study, the boosting algorithm using AdaBoost with Random Forest as the base estimator showed the highest accuracy of 98%, with balanced precision and recall. The bagging technique, which also uses Random Forest as the base estimator, achieved 97% accuracy, although slightly lower than boosting. The stacking technique, which combines XGBoost, Gradient Boosting, and Random Forest as base learners, with Random Forest as the meta-model, yields similar accuracy of 98%, but with lower prediction error, demonstrating its ability to cope with more complex data. Blending, which uses a similar approach but with training on the entire dataset, gave 98% accuracy with shorter processing time and more efficient memory usage than stacking.
Predicting Prospective Student Interests Using the C4.5 Algorithm and Naive Bayes Ritonga, Ali Akbar; Amanda, Annisa; Hasibuan, Elysa Rohayani
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14441

Abstract

Students are individuals pursuing higher education at a university with the goal of enhancing their knowledge, skills, and character to succeed in the professional world and contribute to society. The purpose of this study is to analyze the factors that influence prospective students' interest in continuing their education using the C4.5 Algorithm and the Naïve Bayes Method. The importance of understanding prospective students' interest patterns is expected to help universities formulate more effective strategies. The purpose of this study is to determine how well the two methods classify data and understand the factors that most influence prospective students' decisions. The C4.5 Algorithm is known to be effective in building decision trees that are easy to interpret, while the Naïve Bayes Method has the advantage of handling datasets with independent attributes. This study uses the stages of data selection, data pre-processing, algorithm application, and model evaluation. The classification results obtained from the C4.5 Algorithm show that 132 data are included in the interest category and 8 data are not interested, while the Naïve Bayes Method produces 131 data of interest and 9 data are not interested. In conclusion, both methods have good accuracy levels, but the Naïve Bayes Method shows superiority in Recall value, while the C4.5 Algorithm excels in interpretation of results and clarity of classification patterns.

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